Updates

Model and report changes

  1. The definition of deaths has been adapted to include all deaths that occur in individuals who have had lab-confirmed infection within 60 days from the date of their most recent positive test. This definition reflects more realistically the burden of COVID-19.
  2. Using observations of improved survival in hospitalised COVID-19 patients, we have allowed the probability of dying following infection with SARS-CoV2 (the infection-fatality rate, IFR) to gradually change over the course of June 2020, with a decrease being estimated.
  3. The model uses seroprevalence data on the presence of COVID-19 antibodies in blood samples taken by NHSBT to estimate the levels of cumulative infection within the population over time. As, from early June, the NHSBT has been giving a constantly declining prevalence of antibodies, and these data have been curtailed at this point.
  4. The modelling now accounts for a different susceptibility to infection in the under-15s, using information from literature (Viner et al, 2020) suggesting that children less likely to acquire infection when in contact with an infectious individual.

Updated findings

  1. Our current estimate of the daily number of new infections occurring each day across England is 41,200 (28,500–58,700, 95% credible interval).
  2. The daily number of new infections is particularly high in the Midlands (13,500 infections per day). Note that a substantial proportion of these daily infections will be asymptomatic.
  3. We predict that the number of deaths occurring each day is likely to be between 205 and 317 on the 5th of December.
  4. We estimate Rt to be around 1 in all regions. The probability of Rt exceeding 1 is above 80% only in the South East, while it appears certain that Rt is less than 1 in both the North East and Yorkshire and the North West
  5. The growth rate for England is estimated to be down to -0.01 (-0.02–0.01, 95% credible interval) per day. This means that, nationally, the number of infections has stopped growing with clear decreases in the North West and North East and Yorkshire.
  6. London, followed by the North West, continues to have the highest attack rate, that is the proportion of the population who have ever been infected, though these have been revised down to 19% and 18% respectively. The South West continues to have the lowest attack rate (4%).
  7. Note that the deaths data used are only weakly informative on Rt over the last two weeks and are still occurring in relatively small numbers in some regions. Therefore, the estimate for current incidence, Rt and the forecast of daily numbers of deaths are likely to be subject to some revision.

Interpretation

The plots of Rt over time show a clear downward trend since early-November, with estimates of Rt lower in all regions in comparison to last week. These lower values of Rt are likely to be the combined result of various social distancing interventions, detected through the Google mobility data, and half-term school closures.

The number of new infections is decreasing in both the North East and Yorkshire, and the North West, but continuing to increase in the South East. In all other regions, incidence has plateaued.

We believe that the number of deaths occurring each day has already reached a peak in most regions. However, the number of deaths reported publicly each day may continue to increase for some weeks; these reports are based on deaths identified during the previous 24-hours and also include a number of delayed reports of deaths that had occurred over previous days (for more information see nowcasting COVID-19 deaths).

The lock-down introduced on the 5th of November will have induced changes in contact patterns, which cannot be quantified with any certainty at this point, but have the potential to induce a continued decrease in the Rt values and the number of new infections. Further changes will be reflected in the weekly iterations of our model.

Summary

Real-time tracking of an epidemic, as data accumulate over time, is an essential component of a public health response to a new outbreak. A team of statistical modellers at the MRC Biostatistics Unit (BSU), University of Cambridge, are working to provide regular now-casts and forecasts of COVID-19 infections and deaths. This information feeds directly to the SAGE sub-group, Scientific Pandemic Influenza sub-group on Modelling (SPI-M), and to regional Public Health England (PHE) teams.

Methods

We fit a transmission model (Birrell et al. 2020) to a number of data sources (see ‘Data Sources’), to reconstruct the number of new COVID-19 infections over time in different age groups and NHS regions, estimate a measure of ongoing transmission and predict the number of new COVID-19 deaths.

Data sources

We use:

  1. Data on COVID-19 confirmed deaths from the Public Health England (PHE) line-listing This consists of a combination of deaths notified to:
    • the Demographics Batch Service (DBS), a mechanism that allows PHE to submit a file of patient information to the National Health Service spine for tracing against the personal demographics service (PDS). PHE submit a line list of patients diagnosed with COVID-19 to DBS daily. The file is returned with a death flag and date of death updated (started 20th March, 2020).
    • NHS England, who report data from NHS trusts relating to patients who have died after admission to hospital or within emergency department settings.
    • Health Protection Teams (HPTs), resulting from a select survey created by PHE to capture deaths occurring outside of hospital settings, e.g. care homes (started 23rd March, 2020)
  2. Data on antibody prevalence in blood samples from a PHE survey of NHS Blood Transfusion (NHSBT) donors.

Data are stratified into eight age groups: <1, 1-4, 5-14, 15-24, 25-44, 45-64, 65-74, 75+, and the NHS England regions (North East and Yorkshire, North West, Midlands, East of England, London, South East, South West).

  1. Published information on the the natural history of COVID-19 (Verity et al., 2020; Li et al, 2020)
  2. Information on contacts between different age groups from:
    • A Survey that describes relative rates of contacts between different age groups (Mossong et al. 2008).
    • Google Community Mobility reports, informing the changes in people’s mobility over the course of the pandemic, particularly after the March 23rd lockdown measures.
    • The ONS’ time use survey, which in conjunction with the google mobility study, allows estimation of the changing exposure to infection risk over time.
    • Data from the Department for Education describing the proportion of children currently attending school.

Epidemic summary

Current \(R_t\)

Value of \(R_t\), the average number of secondary infections due to a typical infection today.

Number of infections

Attack rate

The percentage of a given group that has been infected.

By region

By age

IFR

Change in infections incidence

Growth rates

NB: negative growth rates are rates of decline. Values are daily changes.

Region Median 95% CrI (lower) 95% CrI (upper)
England -0.01 -0.02 0.01
East of England -0.02 -0.06 0.02
London 0.00 -0.03 0.03
Midlands 0.00 -0.03 0.02
North East and Yorkshire -0.03 -0.05 -0.01
North West -0.06 -0.08 -0.03
South East 0.01 -0.02 0.05
South West 0.00 -0.04 0.03

Halving times

Halving times in days, if a region shows growth than value will be NA.

Region Median 95% CrI (lower) 95% CrI (upper)
England 106.20 34.16 NA
East of England 38.52 11.87 NA
London NA 21.48 NA
Midlands 586.11 26.24 NA
North East and Yorkshire 21.80 12.72 64.78
North West 11.77 8.25 19.79
South East NA 41.37 NA
South West 613.54 18.71 NA

Doubling times

Doubling times in days, if a region shows decline then the value will be NA.

Region Median 95% CrI (lower) 95% CrI (upper)
England NA 86.90 NA
East of England NA 39.61 NA
London 551.04 21.35 NA
Midlands NA 37.93 NA
North East and Yorkshire NA NA NA
North West NA NA NA
South East 46.27 14.83 NA
South West NA 20.00 NA

Change in deaths incidence

Growth rates

NB: negative growth rates are rates of decline. Values are daily changes.

Region Median 95% CrI (lower) 95% CrI (upper)
England -0.02 -0.02 -0.01
East of England -0.02 -0.04 0.00
London -0.01 -0.03 0.02
Midlands -0.01 -0.02 0.00
North East and Yorkshire -0.02 -0.03 -0.01
North West -0.04 -0.05 -0.02
South East 0.00 -0.01 0.03
South West 0.00 -0.02 0.02

Halving times

Halving times in days, if a region shows growth than value will be NA.

Region Median 95% CrI (lower) 95% CrI (upper)
England 41.87 30.61 72.12
East of England 32.17 17.28 697.35
London 119.58 24.83 NA
Midlands 62.46 27.48 NA
North East and Yorkshire 35.32 21.94 91.89
North West 18.95 14.49 28.63
South East NA 46.41 NA
South West 207.37 27.90 NA

Doubling times

Doubling times in days, if a region shows decline then the value will be NA.

Region Median 95% CrI (lower) 95% CrI (upper)
England NA NA NA
East of England NA NA NA
London NA 36.49 NA
Midlands NA 234.08 NA
North East and Yorkshire NA NA NA
North West NA NA NA
South East 142.67 25.75 NA
South West NA 35.17 NA

Infections and deaths

The blue lines is show when interventions have been introduced (lockdown on 23 Mar and the relaxation of measures on 11 May), and the red line shows the date these results were produced (22 Nov).

Infection incidence

By region

By age

Cumulative infections

By region

By age

Deaths incidence

By region

By age

Cumulative deaths

By region

By age

Prob \(R_t > 1\)

The figure below shows the probability that \(R_t\) is greater than 1 (ie: the number of infections is growing) in each region over time. Clicking the regions in the legend allows lines to be added or removed from the figure.

\(R_t\)

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